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Athalye C, Bahena A, Khandelwal P, Emrani S, Trotman W, Levorse LM, Khodakarami Z, Ohm DT, Teunissen-Bermeo E, Capp N, Sadaghiani S, Arezoumandan S, Lim SA, Prabhakaran K, Ittyerah R, Robinson JL, Schuck T, Lee EB, Tisdall MD, Das SR, Wolk DA, Irwin DJ, Yushkevich PA. Operationalizing postmortem pathology-MRI association studies in Alzheimer's disease and related disorders with MRI-guided histology sampling. Acta Neuropathol Commun 2025; 13:120. [PMID: 40437594 PMCID: PMC12121285 DOI: 10.1186/s40478-025-02030-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2025] [Accepted: 05/05/2025] [Indexed: 06/01/2025] Open
Abstract
Postmortem neuropathological examination, while the gold standard for diagnosing neurodegenerative diseases, often relies on limited regional sampling that may miss critical areas affected by Alzheimer's disease and related disorders. Ultra-high resolution postmortem MRI can help identify regions that fall outside the diagnostic sampling criteria for additional histopathologic evaluation. However, there are no standardized guidelines for integrating histology and MRI in a traditional brain bank. We developed a comprehensive protocol for whole hemisphere postmortem 7T MRI-guided histopathological sampling with whole-slide digital imaging and histopathological analysis, providing a reliable pipeline for high-volume brain banking in heterogeneous brain tissue. Our method uses patient-specific 3D printed molds built from postmortem MRI, allowing standardized tissue processing with a permanent spatial reference frame. To facilitate pathology-MRI association studies, we created a semi-automated MRI to histology registration pipeline and developed a quantitative pathology scoring system using weakly supervised deep learning. We validated this protocol on a cohort of 29 brains with diagnosis on the AD spectrum that revealed correlations between cortical thickness and phosphorylated tau accumulation. This pipeline has broad applicability across neuropathological research and brain banking, facilitating large-scale studies that integrate histology with neuroimaging. The innovations presented here provide a scalable and reproducible approach to studying postmortem brain pathology, with implications for advancing diagnostic and therapeutic strategies for Alzheimer's disease and related disorders.
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Affiliation(s)
- Chinmayee Athalye
- Department of Bioengineering, University of Pennsylvania, Philadelphia, USA.
| | - Alejandra Bahena
- Department of Neurology, University of Pennsylvania, Philadelphia, USA
| | - Pulkit Khandelwal
- Department of Bioengineering, University of Pennsylvania, Philadelphia, USA
| | - Sheina Emrani
- Department of Neurology, University of Pennsylvania, Philadelphia, USA
| | - Winifred Trotman
- Department of Neurology, University of Pennsylvania, Philadelphia, USA
| | - Lisa M Levorse
- Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Zahra Khodakarami
- Department of Bioengineering, University of Pennsylvania, Philadelphia, USA
| | - Daniel T Ohm
- Department of Neurology, University of Pennsylvania, Philadelphia, USA
| | | | - Noah Capp
- Department of Neurology, University of Pennsylvania, Philadelphia, USA
| | | | | | - Sydney A Lim
- Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | | | - Ranjit Ittyerah
- Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - John L Robinson
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, USA
| | - Theresa Schuck
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, USA
| | - Edward B Lee
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, USA
| | - M Dylan Tisdall
- Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Sandhitsu R Das
- Department of Neurology, University of Pennsylvania, Philadelphia, USA
| | - David A Wolk
- Department of Neurology, University of Pennsylvania, Philadelphia, USA
| | - David J Irwin
- Department of Neurology, University of Pennsylvania, Philadelphia, USA
| | - Paul A Yushkevich
- Department of Radiology, University of Pennsylvania, Philadelphia, USA
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2
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Zhao Z, Cao Z, Zhu Q, Xu H, Li S, Zhu L, Xu G, Zhu K, Zhang J, Wu D. Layer-Dependent Effect of Aβ-Pathology on Cortical Microstructure With Ex Vivo Human Brain Diffusion MRI at 7 Tesla. Hum Brain Mapp 2025; 46:e70222. [PMID: 40317841 PMCID: PMC12046383 DOI: 10.1002/hbm.70222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Revised: 04/07/2025] [Accepted: 04/21/2025] [Indexed: 05/07/2025] Open
Abstract
The laminar-specific distributions of Aβ and Tau deposition in the neocortex of Alzheimer's disease (AD) have been established. However, direct evidence about the effect of AD pathology on cortical microstructure is lacking in human studies. We performed high-resolution T2-weighted and diffusion-weighted MRI (dMRI) on 15 ex vivo whole-hemisphere specimens, including eight cases with low AD neuropathologic change, three cases with primary age-related tauopathy (PART), and four healthy controls (HCs). Using the diffusion tensor model, we evaluated microstructure patterns in six layers of gray matter cortex and performed MRI-histology correlation analysis across cortical layers. Aβ-positive cases exhibited higher diffusivity than Aβ-negative cases (PART and HC) in selected cortical regions, particularly in the inferior frontal cortex. Both Aβ/Tau depositions and dMRI-based microstructural markers demonstrated distinct cortical layer-dependent and region-specific patterns. A significant positive correlation was observed between increased diffusivity and Aβ burden across six cortical layers but not with Tau burden. Furthermore, the mean diffusivity in layer V of the inferior frontal cortex significantly increased with the Amyloid stage. Our findings demonstrate a layer-dependent effect of Aβ pathology on cortical microstructure of the human brain, which may be used to serve as a marker of low AD neuropathologic change.
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Affiliation(s)
- Zhiyong Zhao
- Children's HospitalZhejiang University School of Medicine, National Clinical Research Center for Child HealthHangzhouChina
| | - Zuozhen Cao
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical EngineeringCollege of Biomedical Engineering & Instrument Science, Zhejiang UniversityHangzhouChina
| | - Qinfeng Zhu
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical EngineeringCollege of Biomedical Engineering & Instrument Science, Zhejiang UniversityHangzhouChina
| | - Haoan Xu
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical EngineeringCollege of Biomedical Engineering & Instrument Science, Zhejiang UniversityHangzhouChina
| | - Sihui Li
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical EngineeringCollege of Biomedical Engineering & Instrument Science, Zhejiang UniversityHangzhouChina
| | - Liangying Zhu
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical EngineeringCollege of Biomedical Engineering & Instrument Science, Zhejiang UniversityHangzhouChina
| | - Guojun Xu
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical EngineeringCollege of Biomedical Engineering & Instrument Science, Zhejiang UniversityHangzhouChina
| | - Keqing Zhu
- National Human Brain Bank for Health and DiseaseZhejiang UniversityHangzhouChina
| | - Jing Zhang
- National Human Brain Bank for Health and DiseaseZhejiang UniversityHangzhouChina
- Department of PathologyThe First Affiliated Hospital and School of Medicine, Zhejiang UniversityHangzhouChina
| | - Dan Wu
- Children's HospitalZhejiang University School of Medicine, National Clinical Research Center for Child HealthHangzhouChina
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical EngineeringCollege of Biomedical Engineering & Instrument Science, Zhejiang UniversityHangzhouChina
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De Marchi F, Spinelli EG, Bendotti C. Neuroglia in neurodegeneration: Amyotrophic lateral sclerosis and frontotemporal dementia. HANDBOOK OF CLINICAL NEUROLOGY 2025; 210:45-67. [PMID: 40148057 DOI: 10.1016/b978-0-443-19102-2.00004-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/29/2025]
Abstract
Amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) are devastating neurodegenerative diseases sharing significant pathologic and genetic overlap, leading to consider these diseases as a continuum in the spectrum of their pathologic features. Although FTD compromises only specific brain districts, while ALS involves both the nervous system and the skeletal muscles, several neurocentric mechanisms are in common between ALS and FTD. Also, recent research has revealed the significant involvement of nonneuronal cells, particularly glial cells such as astrocytes, oligodendrocytes, microglia, and peripheral immune cells, in disease pathology. This chapter aims to provide an extensive overview of the current understanding of the role of glia in the onset and advancement of ALS and FTD, highlighting the recent implications in terms of prognosis and future treatment options.
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Affiliation(s)
- Fabiola De Marchi
- ALS Centre, Neurology Unit, Maggiore della Carità Hospital, University of Piemonte Orientale, Novara, Italy
| | - Edoardo Gioele Spinelli
- Neurology Unit, Department of Neuroscience, IRCCS Ospedale San Raffaele, Milano, Italy; Vita-Salute San Raffaele University, Milano, Italy
| | - Caterina Bendotti
- Laboratory of Neurobiology and Preclinical Therapeutics, ALS Center, Department of Neuroscience, IRCCS-"Mario Negri" Institute for Pharmacological Research, Milano, Italy.
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Ferretti S, Zanella I. The Underestimated Role of Iron in Frontotemporal Dementia: A Narrative Review. Int J Mol Sci 2024; 25:12987. [PMID: 39684697 DOI: 10.3390/ijms252312987] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2024] [Revised: 11/25/2024] [Accepted: 12/02/2024] [Indexed: 12/18/2024] Open
Abstract
The term frontotemporal dementia (FTD) comprises a group of neurodegenerative disorders characterized by the progressive degeneration of the frontal and temporal lobes of the brain with language impairment and changes in cognitive, behavioral and executive functions, and in some cases motor manifestations. A high proportion of FTD cases are due to genetic mutations and inherited in an autosomal-dominant manner with variable penetrance depending on the implicated gene. Iron is a crucial microelement that is involved in several cellular essential functions in the whole body and plays additional specialized roles in the central nervous system (CNS) mainly through its redox-cycling properties. Such a feature may be harmful under aerobic conditions, since it may lead to the generation of highly reactive hydroxyl radicals. Dysfunctions of iron homeostasis in the CNS are indeed involved in several neurodegenerative disorders, although it is still challenging to determine whether the dyshomeostasis of this essential but harmful metal is a direct cause of neurodegeneration, a contributor factor or simply a consequence of other neurodegenerative mechanisms. Unlike many other neurodegenerative disorders, evidence of the dysfunction in brain iron homeostasis in FTD is still scarce; nonetheless, the recent literature intriguingly suggests its possible involvement. The present review aims to summarize what is currently known about the contribution of iron dyshomeostasis in FTD based on clinical, imaging, histological, biochemical and molecular studies, further suggesting new perspectives and offering new insights for future investigations on this underexplored field of research.
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Affiliation(s)
- Sara Ferretti
- Department of Molecular and Translational Medicine, University of Brescia, 25123 Brescia, Italy
| | - Isabella Zanella
- Department of Molecular and Translational Medicine, University of Brescia, 25123 Brescia, Italy
- Medical Genetics Laboratory, Diagnostic Department, ASST Spedali Civili di Brescia, 25123 Brescia, Italy
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Mihailescu S, Hlava Q, Cook PA, Mandelli ML, Lee SE, Boeve BF, Dickerson BC, Gorno-Tempini ML, Rogalski E, Grossman M, Gee J, McMillan CT, Olm CA. Boundary-based registration improves sensitivity for detecting hypoperfusion in sporadic frontotemporal lobar degeneration. Front Neurol 2024; 15:1452944. [PMID: 39233675 PMCID: PMC11371585 DOI: 10.3389/fneur.2024.1452944] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2024] [Accepted: 08/07/2024] [Indexed: 09/06/2024] Open
Abstract
Introduction Frontotemporal lobar degeneration (FTLD) is associated with FTLD due to tau (FTLD-tau) or TDP (FTLD-TDP) inclusions found at autopsy. Arterial Spin Labeling (ASL) MRI is often acquired in the same session as a structural T1-weighted image (T1w), enabling detection of regional changes in cerebral blood flow (CBF). We hypothesize that ASL-T1w registration with more degrees of freedom using boundary-based registration (BBR) will better align ASL and T1w images and show increased sensitivity to regional hypoperfusion differences compared to manual registration in patient participants. We hypothesize that hypoperfusion will be associated with a clinical measure of disease severity, the FTLD-modified clinical dementia rating scale sum-of-boxes (FTLD-CDR). Materials and methods Patients with sporadic likely FTLD-tau (sFTLD-tau; N = 21), with sporadic likely FTLD-TDP (sFTLD-TDP; N = 14), and controls (N = 50) were recruited from the Connectomic Imaging in Familial and Sporadic Frontotemporal Degeneration project (FTDHCP). Pearson's Correlation Coefficients (CC) were calculated on cortical vertex-wise CBF between each participant for each of 3 registration methods: (1) manual registration, (2) BBR initialized with manual registration (manual+BBR), (3) and BBR initialized using FLIRT (FLIRT+BBR). Mean CBF was calculated in the same regions of interest (ROIs) for each registration method after image alignment. Paired t-tests of CC values for each registration method were performed to compare alignment. Mean CBF in each ROI was compared between groups using t-tests. Differences were considered significant at p < 0.05 (Bonferroni-corrected). We performed linear regression to relate FTLD-CDR to mean CBF in patients with sFTLD-tau and sFTLD-TDP, separately (p < 0.05, uncorrected). Results All registration methods demonstrated significant hypoperfusion in frontal and temporal regions in each patient group relative to controls. All registration methods detected hypoperfusion in the left insular cortex, middle temporal gyrus, and temporal pole in sFTLD-TDP relative to sFTLD-tau. FTLD-CDR had an inverse association with CBF in right temporal and orbitofrontal ROIs in sFTLD-TDP. Manual+BBR performed similarly to FLIRT+BBR. Discussion ASL is sensitive to distinct regions of hypoperfusion in patient participants relative to controls, and in patients with sFTLD-TDP relative to sFTLD-tau, and decreasing perfusion is associated with increasing disease severity, at least in sFTLD-TDP. BBR can register ASL-T1w images adequately for controls and patients.
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Affiliation(s)
- Sylvia Mihailescu
- School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, United States
| | - Quinn Hlava
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - Philip A Cook
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States
| | - Maria Luisa Mandelli
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, United States
| | - Suzee E Lee
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, United States
| | - Bradley F Boeve
- Department of Neurology, Mayo Clinic, Rochester, MN, United States
| | - Bradford C Dickerson
- Department of Neurology, Massachusetts General Hospital, Boston, MA, United States
| | - Maria Luisa Gorno-Tempini
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, United States
| | - Emily Rogalski
- Healthy Aging & Alzheimer's Care Center, University of Chicago, Chicago, IL, United States
- Department of Neurology, University of Chicago, Chicago, IL, United States
| | - Murray Grossman
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - James Gee
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States
| | - Corey T McMillan
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - Christopher A Olm
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
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6
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Das SR, Ilesanmi A, Wolk DA, Gee JC. Beyond Macrostructure: Is There a Role for Radiomics Analysis in Neuroimaging ? Magn Reson Med Sci 2024; 23:367-376. [PMID: 38880615 PMCID: PMC11234947 DOI: 10.2463/mrms.rev.2024-0053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2024] [Accepted: 05/20/2024] [Indexed: 06/18/2024] Open
Abstract
The most commonly used neuroimaging biomarkers of brain structure, particularly in neurodegenerative diseases, have traditionally been summary measurements from ROIs derived from structural MRI, such as volume and thickness. Advances in MR acquisition techniques, including high-field imaging, and emergence of learning-based methods have opened up opportunities to interrogate brain structure in finer detail, allowing investigators to move beyond macrostructural measurements. On the one hand, superior signal contrast has the potential to make appearance-based metrics that directly analyze intensity patterns, such as texture analysis and radiomics features, more reliable. Quantitative MRI, particularly at high-field, can also provide a richer set of measures with greater interpretability. On the other hand, use of neural networks-based techniques has the potential to exploit subtle patterns in images that can now be mined with advanced imaging. Finally, there are opportunities for integration of multimodal data at different spatial scales that is enabled by developments in many of the above techniques-for example, by combining digital histopathology with high-resolution ex-vivo and in-vivo MRI. Some of these approaches are at early stages of development and present their own set of challenges. Nonetheless, they hold promise to drive the next generation of validation and biomarker studies. This article will survey recent developments in this area, with a particular focus on Alzheimer's disease and related disorders. However, most of the discussion is equally relevant to imaging of other neurological disorders, and even to other organ systems of interest. It is not meant to be an exhaustive review of the available literature, but rather presented as a summary of recent trends through the discussion of a collection of representative studies with an eye towards what the future may hold.
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Affiliation(s)
- Sandhitsu R. Das
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
- Penn Memory Center, University of Pennsylvania, Philadelphia, PA, USA
| | - Ademola Ilesanmi
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - David A. Wolk
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
- Penn Memory Center, University of Pennsylvania, Philadelphia, PA, USA
| | - James C. Gee
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
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7
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Khandelwal P, Duong MT, Sadaghiani S, Lim S, Denning AE, Chung E, Ravikumar S, Arezoumandan S, Peterson C, Bedard M, Capp N, Ittyerah R, Migdal E, Choi G, Kopp E, Loja B, Hasan E, Li J, Bahena A, Prabhakaran K, Mizsei G, Gabrielyan M, Schuck T, Trotman W, Robinson J, Ohm DT, Lee EB, Trojanowski JQ, McMillan C, Grossman M, Irwin DJ, Detre JA, Tisdall MD, Das SR, Wisse LEM, Wolk DA, Yushkevich PA. Automated deep learning segmentation of high-resolution 7 Tesla postmortem MRI for quantitative analysis of structure-pathology correlations in neurodegenerative diseases. IMAGING NEUROSCIENCE (CAMBRIDGE, MASS.) 2024; 2:1-30. [PMID: 39301426 PMCID: PMC11409836 DOI: 10.1162/imag_a_00171] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 04/01/2024] [Accepted: 04/15/2024] [Indexed: 09/22/2024]
Abstract
Postmortem MRI allows brain anatomy to be examined at high resolution and to link pathology measures with morphometric measurements. However, automated segmentation methods for brain mapping in postmortem MRI are not well developed, primarily due to limited availability of labeled datasets, and heterogeneity in scanner hardware and acquisition protocols. In this work, we present a high-resolution dataset of 135 postmortem human brain tissue specimens imaged at 0.3 mm3 isotropic using a T2w sequence on a 7T whole-body MRI scanner. We developed a deep learning pipeline to segment the cortical mantle by benchmarking the performance of nine deep neural architectures, followed by post-hoc topological correction. We evaluate the reliability of this pipeline via overlap metrics with manual segmentation in 6 specimens, and intra-class correlation between cortical thickness measures extracted from the automatic segmentation and expert-generated reference measures in 36 specimens. We also segment four subcortical structures (caudate, putamen, globus pallidus, and thalamus), white matter hyperintensities, and the normal appearing white matter, providing a limited evaluation of accuracy. We show generalizing capabilities across whole-brain hemispheres in different specimens, and also on unseen images acquired at 0.28 mm3 and 0.16 mm3 isotropic T2*w fast low angle shot (FLASH) sequence at 7T. We report associations between localized cortical thickness and volumetric measurements across key regions, and semi-quantitative neuropathological ratings in a subset of 82 individuals with Alzheimer's disease (AD) continuum diagnoses. Our code, Jupyter notebooks, and the containerized executables are publicly available at the project webpage (https://pulkit-khandelwal.github.io/exvivo-brain-upenn/).
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Affiliation(s)
- Pulkit Khandelwal
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, United States
- Penn Image Computing and Science Laboratory, University of Pennsylvania, Philadelphia, PA, United States
| | - Michael Tran Duong
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, United States
| | - Shokufeh Sadaghiani
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - Sydney Lim
- Penn Image Computing and Science Laboratory, University of Pennsylvania, Philadelphia, PA, United States
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States
| | - Amanda E. Denning
- Penn Image Computing and Science Laboratory, University of Pennsylvania, Philadelphia, PA, United States
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States
| | - Eunice Chung
- Penn Image Computing and Science Laboratory, University of Pennsylvania, Philadelphia, PA, United States
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States
| | - Sadhana Ravikumar
- Penn Image Computing and Science Laboratory, University of Pennsylvania, Philadelphia, PA, United States
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States
| | - Sanaz Arezoumandan
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - Claire Peterson
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - Madigan Bedard
- Penn Image Computing and Science Laboratory, University of Pennsylvania, Philadelphia, PA, United States
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States
| | - Noah Capp
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - Ranjit Ittyerah
- Penn Image Computing and Science Laboratory, University of Pennsylvania, Philadelphia, PA, United States
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States
| | - Elyse Migdal
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - Grace Choi
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - Emily Kopp
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - Bridget Loja
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - Eusha Hasan
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - Jiacheng Li
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - Alejandra Bahena
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - Karthik Prabhakaran
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States
| | - Gabor Mizsei
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States
| | - Marianna Gabrielyan
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - Theresa Schuck
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Winifred Trotman
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - John Robinson
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Daniel T. Ohm
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - Edward B. Lee
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - John Q. Trojanowski
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Corey McMillan
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - Murray Grossman
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - David J. Irwin
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - John A. Detre
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - M. Dylan Tisdall
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States
| | - Sandhitsu R. Das
- Penn Image Computing and Science Laboratory, University of Pennsylvania, Philadelphia, PA, United States
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | | | - David A. Wolk
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - Paul A. Yushkevich
- Penn Image Computing and Science Laboratory, University of Pennsylvania, Philadelphia, PA, United States
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States
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8
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Mueller SG. 7T MP2RAGE for cortical myelin segmentation: Impact of aging. PLoS One 2024; 19:e0299670. [PMID: 38626149 PMCID: PMC11020839 DOI: 10.1371/journal.pone.0299670] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Accepted: 02/14/2024] [Indexed: 04/18/2024] Open
Abstract
BACKGROUND Myelin and iron are major contributors to the cortical MR signal. The aim of this study was to investigate 1. Can MP2RAGE-derived contrasts at 7T in combination with k-means clustering be used to distinguish between heavily and sparsely myelinated layers in cortical gray matter (GM)? 2. Does this approach provide meaningful biological information? METHODS The following contrasts were generated from the 7T MP2RAGE images from 45 healthy controls (age: 19-75, f/m = 23/22) from the ATAG data repository: 1. T1 weighted image (UNI). 2. T1 relaxation image (T1map). 3. INVC/T1map ratio (RATIO). K-means clustering identified 6 clusters/tissue maps (csf, csf/gm-transition, wm, wm/gm transition, heavily myelinated cortical GM (dGM), sparsely myelinated cortical GM (sGM)). These tissue maps were then processed with SPM/DARTEL (volume-based analyses) and Freesurfer (surface-based analyses) and dGM and sGM volume/thickness of young adults (n = 27, 19-27 years) compared to those of older adults (n = 18, 42-75 years) at p<0.001 uncorrected. RESULTS The resulting maps showed good agreement with histological maps in the literature. Volume- and surface analyses found age-related dGM loss/thinning in the mid-posterior cingulate and parahippocampal/entorhinal gyrus and age-related sGM losses in lateral, mesial and orbitofrontal frontal, insular cortex and superior temporal gyrus. CONCLUSION The MP2RAGE derived UNI, T1map and RATIO contrasts can be used to identify dGM and sGM. Considering the close relationship between cortical myelo- and cytoarchitecture, the findings reported here indicate that this new technique might provide new insights into the nature of cortical GM loss in physiological and pathological conditions.
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Affiliation(s)
- Susanne G. Mueller
- Dept. of Radiology, University of California, San Francisco, San Francisco, CA, United States of America
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9
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Joshi J, Yao M, Kakazu A, Ouyang Y, Duan W, Aggarwal M. Distinguishing microgliosis and tau deposition in the mouse brain using paramagnetic and diamagnetic susceptibility source separation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.11.588962. [PMID: 38659855 PMCID: PMC11042227 DOI: 10.1101/2024.04.11.588962] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
Tauopathies, including Alzheimer's disease (AD), are neurodegenerative disorders characterized by hyperphosphorylated tau protein aggregates in the brain. In addition to protein aggregates, microglia-mediated inflammation and iron dyshomeostasis are other pathological features observed in AD and other tauopathies. It is known that these alterations at the subcellular level occur much before the onset of macroscopic tissue atrophy or cognitive deficits. The ability to detect these microstructural changes with MRI therefore has substantive importance for improved characterization of disease pathogenesis. In this study, we demonstrate that quantitative susceptibility mapping (QSM) with paramagnetic and diamagnetic susceptibility source separation has the potential to distinguish neuropathological alterations in a transgenic mouse model of tauopathy. 3D multi-echo gradient echo data were acquired from fixed brains of PS19 (Tau) transgenic mice and age-matched wild-type (WT) mice (n = 5 each) at 11.7 T. The multi-echo data were fit to a 3-pool complex signal model to derive maps of paramagnetic component susceptibility (PCS) and diamagnetic component susceptibility (DCS). Group-averaged signal fraction and composite susceptibility maps showed significant region-specific differences between the WT and Tau mouse brains. Significant bilateral increases in PCS and |DCS| were observed in specific hippocampal and cortical sub-regions of the Tau mice relative to WT controls. Comparison with immunohistological staining for microglia (Iba1) and phosphorylated-tau (AT8) further indicated that the PCS and DCS differences corresponded to regional microgliosis and tau deposition in the PS19 mouse brains, respectively. The results demonstrate that quantitative susceptibility source separation may provide sensitive imaging markers to detect distinct pathological alterations in tauopathies.
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Affiliation(s)
- Jayvik Joshi
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Minmin Yao
- Division of Neurobiology, Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Aaron Kakazu
- Division of Neurobiology, Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Yuxiao Ouyang
- Division of Neurobiology, Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Wenzhen Duan
- Division of Neurobiology, Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Manisha Aggarwal
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
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10
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Zhang Y, Qian W, Zhang Y, Ma Y, Qian J, Li J, Wei X, Long Y, Wan X. Pediococcus acidilactici reduces tau pathology and ameliorates behavioral deficits in models of neurodegenerative disorders. Cell Commun Signal 2024; 22:84. [PMID: 38291511 PMCID: PMC10826277 DOI: 10.1186/s12964-023-01419-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2023] [Accepted: 12/02/2023] [Indexed: 02/01/2024] Open
Abstract
BACKGROUND Alzheimer's disease (AD), affecting many elders worldwide, is characterized by A-beta and tau-related cognitive decline. Accumulating evidence suggests that brain iron accumulation is an important characteristic of AD. However, the function and mechanism of the iron-mediated gut-brain axis on AD is still unclear. METHODS A Caenorhabditis elegans model with tau-overexpression and a high-Fe diet mouse model of cognitive impairment was used for probiotic function evaluation. With the use of qPCR, and immunoblotting, the probiotic regulated differential expression of AD markers and iron related transporting genes was determined. Colorimetric kits, IHC staining, and immunofluorescence have been performed to explore the probiotic mechanism on the development of gut-brain links and brain iron accumulation. RESULTS In the present study, a high-Fe diet mouse model was used for evaluation in which cognitive impairment, higher A-beta, tau and phosphorylated (p)-tau expression, and dysfunctional phosphate distribution were observed. Considering the close crosstalk between intestine and brain, probiotics were then employed to delay the process of cognitive impairment in the HFe mouse model. Pediococcus acidilactici (PA), but not Bacillus subtilis (BN) administration in HFe-fed mice reduced brain iron accumulation, enhanced global alkaline phosphatase (AP) activity, accelerated dephosphorylation, lowered phosphate levels and increased brain urate production. In addition, because PA regulated cognitive behavior in HFe fed mice, we used the transgenic Caenorhabditis elegans with over-expressed human p-tau for model, and then PA fed worms became more active and longer lived than E.coli fed worms, as well as p-tau was down-regulated. These results suggest that brain iron accumulation influences AD risk proteins and various metabolites. Furthermore, PA was shown to reverse tau-induced pathogenesis via iron transporters and AP-urate interaction. CONCLUSIONS PA administration studies demonstrate that PA is an important mediator of tau protein reduction, p-tau expression and neurodegenerative behavior both in Caenorhabditis elegans and iron-overload mice. Finally, our results provide candidates for AP modulation strategies as preventive tools for promoting brain health. Video Abstract.
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Affiliation(s)
- Yong Zhang
- Shunde Innovation School, Research Institute of Biology and Agriculture, University of Science and Technology Beijing, Beijing, 100083, China
- Zhongzhi International Institute of Agricultural Biosciences, Beijing, 100083, China
- Beijing Engineering Laboratory of Main Crop Bio-Tech Breeding, Beijing International Science and Technology Cooperation Base of Bio-Tech Breeding, Beijing Solidwill Sci-Tech Co. Ltd, Beijing, 100192, China
| | - Weiyi Qian
- Shunde Innovation School, Research Institute of Biology and Agriculture, University of Science and Technology Beijing, Beijing, 100083, China
- Zhongzhi International Institute of Agricultural Biosciences, Beijing, 100083, China
| | - Yitong Zhang
- Shunde Innovation School, Research Institute of Biology and Agriculture, University of Science and Technology Beijing, Beijing, 100083, China
- Zhongzhi International Institute of Agricultural Biosciences, Beijing, 100083, China
| | - Yan Ma
- Shunde Innovation School, Research Institute of Biology and Agriculture, University of Science and Technology Beijing, Beijing, 100083, China
- Zhongzhi International Institute of Agricultural Biosciences, Beijing, 100083, China
| | - Jiamin Qian
- Shunde Innovation School, Research Institute of Biology and Agriculture, University of Science and Technology Beijing, Beijing, 100083, China
- Zhongzhi International Institute of Agricultural Biosciences, Beijing, 100083, China
| | - Jinping Li
- Zhongzhi International Institute of Agricultural Biosciences, Beijing, 100083, China
- Beijing Engineering Laboratory of Main Crop Bio-Tech Breeding, Beijing International Science and Technology Cooperation Base of Bio-Tech Breeding, Beijing Solidwill Sci-Tech Co. Ltd, Beijing, 100192, China
| | - Xun Wei
- Shunde Innovation School, Research Institute of Biology and Agriculture, University of Science and Technology Beijing, Beijing, 100083, China
- Zhongzhi International Institute of Agricultural Biosciences, Beijing, 100083, China
- Beijing Engineering Laboratory of Main Crop Bio-Tech Breeding, Beijing International Science and Technology Cooperation Base of Bio-Tech Breeding, Beijing Solidwill Sci-Tech Co. Ltd, Beijing, 100192, China
| | - Yan Long
- Shunde Innovation School, Research Institute of Biology and Agriculture, University of Science and Technology Beijing, Beijing, 100083, China.
- Zhongzhi International Institute of Agricultural Biosciences, Beijing, 100083, China.
- Beijing Engineering Laboratory of Main Crop Bio-Tech Breeding, Beijing International Science and Technology Cooperation Base of Bio-Tech Breeding, Beijing Solidwill Sci-Tech Co. Ltd, Beijing, 100192, China.
| | - Xiangyuan Wan
- Shunde Innovation School, Research Institute of Biology and Agriculture, University of Science and Technology Beijing, Beijing, 100083, China.
- Zhongzhi International Institute of Agricultural Biosciences, Beijing, 100083, China.
- Beijing Engineering Laboratory of Main Crop Bio-Tech Breeding, Beijing International Science and Technology Cooperation Base of Bio-Tech Breeding, Beijing Solidwill Sci-Tech Co. Ltd, Beijing, 100192, China.
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11
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Sfera A, Andronescu L, Britt WG, Himsl K, Klein C, Rahman L, Kozlakidis Z. Receptor-Independent Therapies for Forensic Detainees with Schizophrenia-Dementia Comorbidity. Int J Mol Sci 2023; 24:15797. [PMID: 37958780 PMCID: PMC10647468 DOI: 10.3390/ijms242115797] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 10/23/2023] [Accepted: 10/28/2023] [Indexed: 11/15/2023] Open
Abstract
Forensic institutions throughout the world house patients with severe psychiatric illness and history of criminal violations. Improved medical care, hygiene, psychiatric treatment, and nutrition led to an unmatched longevity in this population, which previously lived, on average, 15 to 20 years shorter than the public at large. On the other hand, longevity has contributed to increased prevalence of age-related diseases, including neurodegenerative disorders, which complicate clinical management, increasing healthcare expenditures. Forensic institutions, originally intended for the treatment of younger individuals, are ill-equipped for the growing number of older offenders. Moreover, as antipsychotic drugs became available in 1950s and 1960s, we are observing the first generation of forensic detainees who have aged on dopamine-blocking agents. Although the consequences of long-term treatment with these agents are unclear, schizophrenia-associated gray matter loss may contribute to the development of early dementia. Taken together, increased lifespan and the subsequent cognitive deficit observed in long-term forensic institutions raise questions and dilemmas unencountered by the previous generations of clinicians. These include: does the presence of neurocognitive dysfunction justify antipsychotic dose reduction or discontinuation despite a lifelong history of schizophrenia and violent behavior? Should neurolipidomic interventions become the standard of care in elderly individuals with lifelong schizophrenia and dementia? Can patients with schizophrenia and dementia meet the Dusky standard to stand trial? Should neurocognitive disorders in the elderly with lifelong schizophrenia be treated differently than age-related neurodegeneration? In this article, we hypothesize that gray matter loss is the core symptom of schizophrenia which leads to dementia. We hypothesize further that strategies to delay or stop gray matter depletion would not only improve the schizophrenia sustained recovery, but also avert the development of major neurocognitive disorders in people living with schizophrenia. Based on this hypothesis, we suggest utilization of both receptor-dependent and independent therapeutics for chronic psychosis.
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Affiliation(s)
- Adonis Sfera
- Paton State Hospital, 3102 Highland Ave, Patton, CA 92369, USA; (L.A.); (K.H.)
- School of Behavioral Health, Loma Linda University, 11139 Anderson St., Loma Linda, CA 92350, USA
- Department of Psychiatry, University of California, Riverside 900 University Ave, Riverside, CA 92521, USA
| | - Luminita Andronescu
- Paton State Hospital, 3102 Highland Ave, Patton, CA 92369, USA; (L.A.); (K.H.)
| | - William G. Britt
- Department of Psychiatry, School of Medicine, Loma Linda University, Loma Linda, CA 92350, USA;
| | - Kiera Himsl
- Paton State Hospital, 3102 Highland Ave, Patton, CA 92369, USA; (L.A.); (K.H.)
| | - Carolina Klein
- California Department of State Hospitals, Sacramento, CA 95814, USA;
| | - Leah Rahman
- Department of Neuroscience, University of Oregon, 1585 E 13th Ave, Eugene, OR 97403, USA;
| | - Zisis Kozlakidis
- International Agency for Research on Cancer, 69366 Lyon Cedex, France;
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12
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Ohm DT, Rhodes E, Bahena A, Capp N, Lowe M, Sabatini P, Trotman W, Olm CA, Phillips J, Prabhakaran K, Rascovsky K, Massimo L, McMillan C, Gee J, Tisdall MD, Yushkevich PA, Lee EB, Grossman M, Irwin DJ. Neuroanatomical and cellular degeneration associated with a social disorder characterized by new ritualistic belief systems in a TDP-C patient vs. a Pick patient. Front Neurol 2023; 14:1245886. [PMID: 37900607 PMCID: PMC10600461 DOI: 10.3389/fneur.2023.1245886] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Accepted: 08/15/2023] [Indexed: 10/31/2023] Open
Abstract
Frontotemporal dementia (FTD) is a spectrum of clinically and pathologically heterogenous neurodegenerative dementias. Clinical and anatomical variants of FTD have been described and associated with underlying frontotemporal lobar degeneration (FTLD) pathology, including tauopathies (FTLD-tau) or TDP-43 proteinopathies (FTLD-TDP). FTD patients with predominant degeneration of anterior temporal cortices often develop a language disorder of semantic knowledge loss and/or a social disorder often characterized by compulsive rituals and belief systems corresponding to predominant left or right hemisphere involvement, respectively. The neural substrates of these complex social disorders remain unclear. Here, we present a comparative imaging and postmortem study of two patients, one with FTLD-TDP (subtype C) and one with FTLD-tau (subtype Pick disease), who both developed new rigid belief systems. The FTLD-TDP patient developed a complex set of values centered on positivity and associated with specific physical and behavioral features of pigs, while the FTLD-tau patient developed compulsive, goal-directed behaviors related to general themes of positivity and spirituality. Neuroimaging showed left-predominant temporal atrophy in the FTLD-TDP patient and right-predominant frontotemporal atrophy in the FTLD-tau patient. Consistent with antemortem cortical atrophy, histopathologic examinations revealed severe loss of neurons and myelin predominantly in the anterior temporal lobes of both patients, but the FTLD-tau patient showed more bilateral, dorsolateral involvement featuring greater pathology and loss of projection neurons and deep white matter. These findings highlight that the regions within and connected to anterior temporal lobes may have differential vulnerability to distinct FTLD proteinopathies and serve important roles in human belief systems.
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Affiliation(s)
- Daniel T. Ohm
- Penn Digital Neuropathology Laboratory, Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
- Penn Frontotemporal Degeneration Center, Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - Emma Rhodes
- Penn Frontotemporal Degeneration Center, Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - Alejandra Bahena
- Penn Digital Neuropathology Laboratory, Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - Noah Capp
- Penn Digital Neuropathology Laboratory, Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - MaKayla Lowe
- Penn Digital Neuropathology Laboratory, Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - Philip Sabatini
- Penn Digital Neuropathology Laboratory, Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - Winifred Trotman
- Penn Digital Neuropathology Laboratory, Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - Christopher A. Olm
- Penn Frontotemporal Degeneration Center, Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - Jeffrey Phillips
- Penn Frontotemporal Degeneration Center, Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - Karthik Prabhakaran
- Penn Image Computing and Science Lab, Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States
| | - Katya Rascovsky
- Penn Frontotemporal Degeneration Center, Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - Lauren Massimo
- Penn Frontotemporal Degeneration Center, Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - Corey McMillan
- Penn Frontotemporal Degeneration Center, Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - James Gee
- Penn Image Computing and Science Lab, Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States
| | - M. Dylan Tisdall
- Center for Advanced Magnetic Resonance Imaging and Spectroscopy, Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States
| | - Paul A. Yushkevich
- Penn Image Computing and Science Lab, Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States
| | - Edward B. Lee
- Center for Neurodegenerative Disease Research, Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Murray Grossman
- Penn Frontotemporal Degeneration Center, Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - David J. Irwin
- Penn Digital Neuropathology Laboratory, Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
- Penn Frontotemporal Degeneration Center, Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
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13
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Vogel JW, Corriveau-Lecavalier N, Franzmeier N, Pereira JB, Brown JA, Maass A, Botha H, Seeley WW, Bassett DS, Jones DT, Ewers M. Connectome-based modelling of neurodegenerative diseases: towards precision medicine and mechanistic insight. Nat Rev Neurosci 2023; 24:620-639. [PMID: 37620599 DOI: 10.1038/s41583-023-00731-8] [Citation(s) in RCA: 62] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/26/2023] [Indexed: 08/26/2023]
Abstract
Neurodegenerative diseases are the most common cause of dementia. Although their underlying molecular pathologies have been identified, there is substantial heterogeneity in the patterns of progressive brain alterations across and within these diseases. Recent advances in neuroimaging methods have revealed that pathological proteins accumulate along specific macroscale brain networks, implicating the network architecture of the brain in the system-level pathophysiology of neurodegenerative diseases. However, the extent to which 'network-based neurodegeneration' applies across the wide range of neurodegenerative disorders remains unclear. Here, we discuss the state-of-the-art of neuroimaging-based connectomics for the mapping and prediction of neurodegenerative processes. We review findings supporting brain networks as passive conduits through which pathological proteins spread. As an alternative view, we also discuss complementary work suggesting that network alterations actively modulate the spreading of pathological proteins between connected brain regions. We conclude this Perspective by proposing an integrative framework in which connectome-based models can be advanced along three dimensions of innovation: incorporating parameters that modulate propagation behaviour on the basis of measurable biological features; building patient-tailored models that use individual-level information and allowing model parameters to interact dynamically over time. We discuss promises and pitfalls of these strategies for improving disease insights and moving towards precision medicine.
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Affiliation(s)
- Jacob W Vogel
- Department of Clinical Sciences, SciLifeLab, Lund University, Lund, Sweden.
| | - Nick Corriveau-Lecavalier
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA
| | - Nicolai Franzmeier
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Acadamy, University of Gothenburg, Mölndal and Gothenburg, Sweden
| | - Joana B Pereira
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden
- Neuro Division, Department of Clinical Neurosciences, Karolinska Institute, Stockholm, Sweden
| | - Jesse A Brown
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Anne Maass
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Hugo Botha
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - William W Seeley
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
- Department of Pathology, University of California, San Francisco, CA, USA
| | - Dani S Bassett
- Departments of Bioengineering, Electrical and Systems Engineering, Physics and Astronomy, Neurology and Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Santa Fe Institute, Santa Fe, NM, USA
| | - David T Jones
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - Michael Ewers
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany.
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14
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Grossman M, Seeley WW, Boxer AL, Hillis AE, Knopman DS, Ljubenov PA, Miller B, Piguet O, Rademakers R, Whitwell JL, Zetterberg H, van Swieten JC. Frontotemporal lobar degeneration. Nat Rev Dis Primers 2023; 9:40. [PMID: 37563165 DOI: 10.1038/s41572-023-00447-0] [Citation(s) in RCA: 55] [Impact Index Per Article: 27.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 06/12/2023] [Indexed: 08/12/2023]
Abstract
Frontotemporal lobar degeneration (FTLD) is one of the most common causes of early-onset dementia and presents with early social-emotional-behavioural and/or language changes that can be accompanied by a pyramidal or extrapyramidal motor disorder. About 20-25% of individuals with FTLD are estimated to carry a mutation associated with a specific FTLD pathology. The discovery of these mutations has led to important advances in potentially disease-modifying treatments that aim to slow progression or delay disease onset and has improved understanding of brain functioning. In both mutation carriers and those with sporadic disease, the most common underlying diagnoses are linked to neuronal and glial inclusions containing tau (FTLD-tau) or TDP-43 (FTLD-TDP), although 5-10% of patients may have inclusions containing proteins from the FUS-Ewing sarcoma-TAF15 family (FTLD-FET). Biomarkers definitively identifying specific pathological entities in sporadic disease have been elusive, which has impeded development of disease-modifying treatments. Nevertheless, disease-monitoring biofluid and imaging biomarkers are becoming increasingly sophisticated and are likely to serve as useful measures of treatment response during trials of disease-modifying treatments. Symptomatic trials using novel approaches such as transcranial direct current stimulation are also beginning to show promise.
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Affiliation(s)
- Murray Grossman
- Department of Neurology and Penn Frontotemporal Degeneration Center, University of Pennsylvania, Philadelphia, PA, USA
| | - William W Seeley
- Departments of Neurology and Memory and Aging Center, University of California, San Francisco, San Francisco, CA, USA.
- Department of Pathology, University of California, San Francisco, San Francisco, CA, USA.
| | - Adam L Boxer
- Departments of Neurology and Memory and Aging Center, University of California, San Francisco, San Francisco, CA, USA
| | - Argye E Hillis
- Department of Neurology, Johns Hopkins University, Baltimore, MD, USA
| | | | - Peter A Ljubenov
- Departments of Neurology and Memory and Aging Center, University of California, San Francisco, San Francisco, CA, USA
| | - Bruce Miller
- Departments of Neurology and Memory and Aging Center, University of California, San Francisco, San Francisco, CA, USA
| | - Olivier Piguet
- School of Psychology and Brain and Mind Center, University of Sydney, Sydney, New South Wales, Australia
| | - Rosa Rademakers
- VIB Center for Molecular Neurology, University of Antwerp, Antwerp, Belgium
| | | | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The University of Gothenburg, Mölndal, Sweden
- Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
- Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK
- UK Dementia Research Institute at UCL, London, UK
- Hong Kong Center for Neurodegenerative Diseases, Clear Water Bay, Hong Kong, China
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA
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15
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Sadaghiani S, Trotman W, Lim SA, Chung E, Ittyerah R, Ravikumar S, Khandelwal P, Prabhakaran K, Lavery ML, Ohm DT, Gabrielyan M, Das SR, Schuck T, Capp N, Peterson CS, Migdal E, Artacho-Pérula E, del Mar Arroyo Jiménez M, del Pilar Marcos Rabal M, Sánchez SC, de la Rosa Prieto C, Parada MC, Insausti R, Robinson JL, McMillan C, Grossman M, Lee EB, Detre JA, Xie SX, Trojanowski JQ, Tisdall MD, Wisse LEM, Irwin DJ, Wolk DA, Yushkevich PA. Associations of phosphorylated tau pathology with whole-hemisphere ex vivo morphometry in 7 tesla MRI. Alzheimers Dement 2023; 19:2355-2364. [PMID: 36464907 PMCID: PMC10239526 DOI: 10.1002/alz.12884] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 09/29/2022] [Accepted: 10/27/2022] [Indexed: 12/12/2022]
Abstract
INTRODUCTION Neurodegenerative disorders are associated with different pathologies that often co-occur but cannot be measured specifically with in vivo methods. METHODS Thirty-three brain hemispheres from donors with an Alzheimer's disease (AD) spectrum diagnosis underwent T2-weighted magnetic resonance imaging (MRI). Gray matter thickness was paired with histopathology from the closest anatomic region in the contralateral hemisphere. RESULTS Partial Spearman correlation of phosphorylated tau and cortical thickness with TAR DNA-binding protein 43 (TDP-43) and α-synuclein scores, age, sex, and postmortem interval as covariates showed significant relationships in entorhinal and primary visual cortices, temporal pole, and insular and posterior cingulate gyri. Linear models including Braak stages, TDP-43 and α-synuclein scores, age, sex, and postmortem interval showed significant correlation between Braak stage and thickness in the parahippocampal gyrus, entorhinal cortex, and Broadman area 35. CONCLUSION We demonstrated an association of measures of AD pathology with tissue loss in several AD regions despite a limited range of pathology in these cases. HIGHLIGHTS Neurodegenerative disorders are associated with co-occurring pathologies that cannot be measured specifically with in vivo methods. Identification of the topographic patterns of these pathologies in structural magnetic resonance imaging (MRI) may provide probabilistic biomarkers. We demonstrated the correlation of the specific patterns of tissue loss from ex vivo brain MRI with underlying pathologies detected in postmortem brain hemispheres in patients with Alzheimer's disease (AD) spectrum disorders. The results provide insight into the interpretation of in vivo structural MRI studies in patients with AD spectrum disorders.
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Affiliation(s)
- Shokufeh Sadaghiani
- Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Winifred Trotman
- Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sydney A Lim
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Eunice Chung
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Ranjit Ittyerah
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sadhana Ravikumar
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Pulkit Khandelwal
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Karthik Prabhakaran
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Madigan L Lavery
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Daniel T Ohm
- Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Marianna Gabrielyan
- Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sandhitsu R. Das
- Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Theresa Schuck
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Noah Capp
- Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Claire S Peterson
- Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Elyse Migdal
- College of Arts and Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Emilio Artacho-Pérula
- Human Neuroanatomy Laboratory, Neuromax CSIC Associated Unit, University of Castilla-La Mancha, Albacete, Spain
| | | | | | - Sandra Cebada Sánchez
- Human Neuroanatomy Laboratory, Neuromax CSIC Associated Unit, University of Castilla-La Mancha, Albacete, Spain
| | - Carlos de la Rosa Prieto
- Human Neuroanatomy Laboratory, Neuromax CSIC Associated Unit, University of Castilla-La Mancha, Albacete, Spain
| | - Marta Córcoles Parada
- Human Neuroanatomy Laboratory, Neuromax CSIC Associated Unit, University of Castilla-La Mancha, Albacete, Spain
| | - Ricardo Insausti
- Human Neuroanatomy Laboratory, Neuromax CSIC Associated Unit, University of Castilla-La Mancha, Albacete, Spain
| | - John L Robinson
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Corey McMillan
- Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Murray Grossman
- Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Edward B Lee
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - John A. Detre
- Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sharon X. Xie
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - John Q Trojanowski
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - M Dylan Tisdall
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Laura EM Wisse
- Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Diagnostic Radiology, Lund University, 22242 Lund, Sweden
| | - David J Irwin
- Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - David A. Wolk
- Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Paul A. Yushkevich
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
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16
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Valori CF, Sulmona C, Brambilla L, Rossi D. Astrocytes: Dissecting Their Diverse Roles in Amyotrophic Lateral Sclerosis and Frontotemporal Dementia. Cells 2023; 12:1450. [PMID: 37296571 PMCID: PMC10252425 DOI: 10.3390/cells12111450] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 05/04/2023] [Accepted: 05/19/2023] [Indexed: 06/12/2023] Open
Abstract
Amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) are fatal neurodegenerative disorders often co-occurring in the same patient, a feature that suggests a common origin of the two diseases. Consistently, pathological inclusions of the same proteins as well as mutations in the same genes can be identified in both ALS/FTD. Although many studies have described several disrupted pathways within neurons, glial cells are also regarded as crucial pathogenetic contributors in ALS/FTD. Here, we focus our attention on astrocytes, a heterogenous population of glial cells that perform several functions for optimal central nervous system homeostasis. Firstly, we discuss how post-mortem material from ALS/FTD patients supports astrocyte dysfunction around three pillars: neuroinflammation, abnormal protein aggregation, and atrophy/degeneration. Furthermore, we summarize current attempts at monitoring astrocyte functions in living patients using either novel imaging strategies or soluble biomarkers. We then address how astrocyte pathology is recapitulated in animal and cellular models of ALS/FTD and how we used these models both to understand the molecular mechanisms driving glial dysfunction and as platforms for pre-clinical testing of therapeutics. Finally, we present the current clinical trials for ALS/FTD, restricting our discussion to treatments that modulate astrocyte functions, directly or indirectly.
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Affiliation(s)
- Chiara F. Valori
- Molecular Neuropathology of Neurodegenerative Diseases, German Centre for Neurodegenerative Diseases (DZNE), 72072 Tübingen, Germany
- Department of Neuropathology, University of Tübingen, 72076 Tübingen, Germany
| | - Claudia Sulmona
- Laboratory for Research on Neurodegenerative Disorders, Istituti Clinici Scientifici Maugeri IRCCS, 27100 Pavia, Italy
| | - Liliana Brambilla
- Laboratory for Research on Neurodegenerative Disorders, Istituti Clinici Scientifici Maugeri IRCCS, 27100 Pavia, Italy
| | - Daniela Rossi
- Laboratory for Research on Neurodegenerative Disorders, Istituti Clinici Scientifici Maugeri IRCCS, 27100 Pavia, Italy
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17
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Arezoumandan S, Xie SX, Cousins KAQ, Mechanic-Hamilton DJ, Peterson CS, Huang CY, Ohm DT, Ittyerah R, McMillan CT, Wolk DA, Yushkevich P, Trojanowski JQ, Lee EB, Grossman M, Phillips JS, Irwin DJ. Regional distribution and maturation of tau pathology among phenotypic variants of Alzheimer's disease. Acta Neuropathol 2022; 144:1103-1116. [PMID: 35871112 PMCID: PMC9936795 DOI: 10.1007/s00401-022-02472-x] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 07/02/2022] [Accepted: 07/14/2022] [Indexed: 01/26/2023]
Abstract
Alzheimer's disease neuropathologic change (ADNC) is clinically heterogenous and can present with a classic multidomain amnestic syndrome or focal non-amnestic syndromes. Here, we investigated the distribution and burden of phosphorylated and C-terminally cleaved tau pathologies across hippocampal subfields and cortical regions among phenotypic variants of Alzheimer's disease (AD). In this study, autopsy-confirmed patients with ADNC, were classified into amnestic (aAD, N = 40) and non-amnestic (naAD, N = 39) groups based on clinical criteria. We performed digital assessment of tissue sections immunostained for phosphorylated-tau (AT8 detects pretangles and mature tangles), D421-truncated tau (TauC3, a marker for mature tangles and ghost tangles), and E391-truncated tau (MN423, a marker that primarily detects ghost tangles), in hippocampal subfields and three cortical regions. Linear mixed-effect models were used to test regional and group differences while adjusting for demographics. Both groups showed AT8-reactivity across hippocampal subfields that mirrored traditional Braak staging with higher burden of phosphorylated-tau in subregions implicated as affected early in Braak staging. The burden of phosphorylated-tau and TauC3-immunoreactive tau in the hippocampus was largely similar between the aAD and naAD groups. In contrast, the naAD group had lower relative distribution of MN423-reactive tangles in CA1 (β = - 0.2, SE = 0.09, p = 0.001) and CA2 (β = - 0.25, SE = 0.09, p = 0.005) compared to the aAD. While the two groups had similar levels of phosphorylated-tau pathology in cortical regions, there was higher burden of TauC3 reactivity in sup/mid temporal cortex (β = 0.16, SE = 0.07, p = 0.02) and MN423 reactivity in all cortical regions (β = 0.4-0.43, SE = 0.09, p < 0.001) in the naAD compared to aAD. In conclusion, AD clinical variants may have a signature distribution of overall phosphorylated-tau pathology within the hippocampus reflecting traditional Braak staging; however, non-amnestic AD has greater relative mature tangle pathology in the neocortex compared to patients with clinical amnestic AD, where the hippocampus had greatest relative burden of C-terminally cleaved tau reactivity. Thus, varying neuronal susceptibility to tau-mediated neurodegeneration may influence the clinical expression of ADNC.
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Affiliation(s)
- Sanaz Arezoumandan
- Digital Neuropathology Laboratory, Department of Neurology, Perelman School of Medicine, Philadelphia, PA, 19104, USA
- Penn Frontotemporal Degeneration Center, Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | - Sharon X Xie
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Katheryn A Q Cousins
- Penn Frontotemporal Degeneration Center, Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | - Dawn J Mechanic-Hamilton
- Department of Neurology, Penn Memory Center, Perelman School of Medicine, Philadelphia, PA, 19104, USA
- Department of Neurology, Penn Alzheimer's Disease Research Center, Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | - Claire S Peterson
- Digital Neuropathology Laboratory, Department of Neurology, Perelman School of Medicine, Philadelphia, PA, 19104, USA
- Penn Frontotemporal Degeneration Center, Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | - Camille Y Huang
- Digital Neuropathology Laboratory, Department of Neurology, Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | - Daniel T Ohm
- Digital Neuropathology Laboratory, Department of Neurology, Perelman School of Medicine, Philadelphia, PA, 19104, USA
- Penn Frontotemporal Degeneration Center, Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | - Ranjit Ittyerah
- Penn Image Computing and Science Lab, Department of Radiology, Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | - Corey T McMillan
- Penn Frontotemporal Degeneration Center, Perelman School of Medicine, Philadelphia, PA, 19104, USA
- Department of Neurology, Penn Alzheimer's Disease Research Center, Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | - David A Wolk
- Department of Neurology, Penn Memory Center, Perelman School of Medicine, Philadelphia, PA, 19104, USA
- Department of Neurology, Penn Alzheimer's Disease Research Center, Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | - Paul Yushkevich
- Department of Neurology, Penn Alzheimer's Disease Research Center, Perelman School of Medicine, Philadelphia, PA, 19104, USA
- Penn Image Computing and Science Lab, Department of Radiology, Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | - John Q Trojanowski
- Department of Neurology, Penn Alzheimer's Disease Research Center, Perelman School of Medicine, Philadelphia, PA, 19104, USA
- Center for Neurodegenerative Disease Research, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | - Edward B Lee
- Department of Neurology, Penn Alzheimer's Disease Research Center, Perelman School of Medicine, Philadelphia, PA, 19104, USA
- Center for Neurodegenerative Disease Research, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, Philadelphia, PA, 19104, USA
- Translational Neuropathology Research Laboratory, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Murray Grossman
- Penn Frontotemporal Degeneration Center, Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | - Jeffrey S Phillips
- Penn Frontotemporal Degeneration Center, Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | - David J Irwin
- Digital Neuropathology Laboratory, Department of Neurology, Perelman School of Medicine, Philadelphia, PA, 19104, USA.
- Penn Frontotemporal Degeneration Center, Perelman School of Medicine, Philadelphia, PA, 19104, USA.
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18
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Calderón-Garcidueñas L, Stommel EW, Lachmann I, Waniek K, Chao CK, González-Maciel A, García-Rojas E, Torres-Jardón R, Delgado-Chávez R, Mukherjee PS. TDP-43 CSF Concentrations Increase Exponentially with Age in Metropolitan Mexico City Young Urbanites Highly Exposed to PM 2.5 and Ultrafine Particles and Historically Showing Alzheimer and Parkinson's Hallmarks. Brain TDP-43 Pathology in MMC Residents Is Associated with High Cisternal CSF TDP-43 Concentrations. TOXICS 2022; 10:559. [PMID: 36287840 PMCID: PMC9611594 DOI: 10.3390/toxics10100559] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 09/07/2022] [Accepted: 09/15/2022] [Indexed: 06/16/2023]
Abstract
Environmental exposures to fine particulate matter (PM2.5) and ultrafine particle matter (UFPM) are associated with overlapping Alzheimer’s, Parkinson’s and TAR DNA-binding protein 43 (TDP-43) hallmark protein pathologies in young Metropolitan Mexico City (MMC) urbanites. We measured CSF concentrations of TDP-43 in 194 urban residents, including 92 MMC children aged 10.2 ± 4.7 y exposed to PM2.5 levels above the USEPA annual standard and to high UFPM and 26 low pollution controls (11.5 ± 4.4 y); 43 MMC adults (42.3 ± 15.9 y) and 14 low pollution adult controls (33.1 ± 12.0 y); and 19 amyotrophic lateral sclerosis (ALS) patients (52.4 ± 14.1 y). TDP-43 neuropathology and cisternal CSF data from 20 subjects—15 MMC (41.1 ± 18.9 y) and 5 low pollution controls (46 ± 16.01 y)—were included. CSF TDP-43 exponentially increased with age (p < 0.0001) and it was higher for MMC residents. TDP-43 cisternal CSF levels of 572 ± 208 pg/mL in 6/15 MMC autopsy cases forecasted TDP-43 in the olfactory bulb, medulla and pons, reticular formation and motor nuclei neurons. A 16 y old with TDP-43 cisternal levels of 1030 pg/mL exhibited TDP-43 pathology and all 15 MMC autopsy cases exhibited AD and PD hallmarks. Overlapping TDP-43, AD and PD pathologies start in childhood in urbanites with high exposures to PM2.5 and UFPM. Early, sustained exposures to PM air pollution represent a high risk for developing brains and MMC UFPM emissions sources ought to be clearly identified, regulated, monitored and controlled. Prevention of deadly neurologic diseases associated with air pollution ought to be a public health priority and preventive medicine is key.
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Affiliation(s)
- Lilian Calderón-Garcidueñas
- College of Health, The University of Montana, Missoula, MT 59812, USA
- Universidad del Valle de México, Mexico City 14370, Mexico
| | - Elijah W. Stommel
- Department of Neurology, Geisel School of Medicine at Dartmouth, Hanover, NH 03755, USA
| | | | | | - Chih-Kai Chao
- College of Health, The University of Montana, Missoula, MT 59812, USA
| | | | | | - Ricardo Torres-Jardón
- Instituto de Ciencias de la Atmósfera y Cambio Climático, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico
| | | | - Partha S. Mukherjee
- Interdisciplinary Statistical Research Unit, Indian Statistical Institute, Kolkata 700108, India
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19
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Whitmore CA, Haynes JR, Behof WJ, Rosenberg AJ, Tantawy MN, Hachey BC, Wadzinski BE, Spiller BW, Peterson TE, Paffenroth KC, Harrison FE, Beelman RB, Wijesinghe P, Matsubara JA, Pham W. Longitudinal Consumption of Ergothioneine Reduces Oxidative Stress and Amyloid Plaques and Restores Glucose Metabolism in the 5XFAD Mouse Model of Alzheimer's Disease. Pharmaceuticals (Basel) 2022; 15:742. [PMID: 35745661 PMCID: PMC9228400 DOI: 10.3390/ph15060742] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Revised: 05/30/2022] [Accepted: 06/01/2022] [Indexed: 01/27/2023] Open
Abstract
Background: Ergothioneine (ERGO) is a unique antioxidant and a rare amino acid available in fungi and various bacteria but not in higher plants or animals. Substantial research data indicate that ERGO is a physiological antioxidant cytoprotectant. Different from other antioxidants that need to breach the blood-brain barrier to enter the brain parenchyma, a specialized transporter called OCTN1 has been identified for transporting ERGO to the brain. Purpose: To assess whether consumption of ERGO can prevent the progress of Alzheimer's disease (AD) on young (4-month-old) 5XFAD mice. Methods and materials: Three cohorts of mice were tested in this study, including ERGO-treated 5XFAD, non-treated 5XFAD, and WT mice. After the therapy, the animals went through various behavioral experiments to assess cognition. Then, mice were scanned with PET imaging to evaluate the biomarkers associated with AD using [11C]PIB, [11C]ERGO, and [18F]FDG radioligands. At the end of imaging, the animals went through cardiac perfusion, and the brains were isolated for immunohistology. Results: Young (4-month-old) 5XFAD mice did not show a cognitive deficit, and thus, we observed modest improvement in the treated counterparts. In contrast, the response to therapy was clearly detected at the molecular level. Treating 5XFAD mice with ERGO resulted in reduced amyloid plaques, oxidative stress, and rescued glucose metabolism. Conclusions: Consumption of high amounts of ERGO benefits the brain. ERGO has the potential to prevent AD. This work also demonstrates the power of imaging technology to assess response during therapy.
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Affiliation(s)
- Clayton A. Whitmore
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA; (C.A.W.); (J.R.H.); (W.J.B.); (A.J.R.); (M.N.T.); (T.E.P.)
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Justin R. Haynes
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA; (C.A.W.); (J.R.H.); (W.J.B.); (A.J.R.); (M.N.T.); (T.E.P.)
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - William J. Behof
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA; (C.A.W.); (J.R.H.); (W.J.B.); (A.J.R.); (M.N.T.); (T.E.P.)
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Adam J. Rosenberg
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA; (C.A.W.); (J.R.H.); (W.J.B.); (A.J.R.); (M.N.T.); (T.E.P.)
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Mohammed N. Tantawy
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA; (C.A.W.); (J.R.H.); (W.J.B.); (A.J.R.); (M.N.T.); (T.E.P.)
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Brian C. Hachey
- Department of Biochemistry, Vanderbilt University, Nashville, TN 37232, USA;
| | - Brian E. Wadzinski
- Department of Pharmacology, Vanderbilt University, Nashville, TN 37233, USA; (B.E.W.); (B.W.S.); (K.C.P.)
| | - Benjamin W. Spiller
- Department of Pharmacology, Vanderbilt University, Nashville, TN 37233, USA; (B.E.W.); (B.W.S.); (K.C.P.)
| | - Todd E. Peterson
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA; (C.A.W.); (J.R.H.); (W.J.B.); (A.J.R.); (M.N.T.); (T.E.P.)
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Krista C. Paffenroth
- Department of Pharmacology, Vanderbilt University, Nashville, TN 37233, USA; (B.E.W.); (B.W.S.); (K.C.P.)
- Vanderbilt Brain Institute, Vanderbilt University, Nashville, TN 37232, USA;
| | - Fiona E. Harrison
- Vanderbilt Brain Institute, Vanderbilt University, Nashville, TN 37232, USA;
- Department of Medicine, Diabetes, Endocrinology & Metabolism, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University Medical Center, Nashville, TN 37212, USA
| | - Robert B. Beelman
- Department of Food Science, Center for Plant and Mushroom Foods for Health, Penn State University, University Park, PA 16802, USA;
| | - Printha Wijesinghe
- Department of Ophthalmology and Visual Sciences, University of British Columbia, Vancouver, BC V5Z 3N9, Canada; (P.W.); (J.A.M.)
| | - Joanne A. Matsubara
- Department of Ophthalmology and Visual Sciences, University of British Columbia, Vancouver, BC V5Z 3N9, Canada; (P.W.); (J.A.M.)
| | - Wellington Pham
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA; (C.A.W.); (J.R.H.); (W.J.B.); (A.J.R.); (M.N.T.); (T.E.P.)
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Vanderbilt Brain Institute, Vanderbilt University, Nashville, TN 37232, USA;
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University Medical Center, Nashville, TN 37212, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235, USA
- Vanderbilt Ingram Cancer Center, Nashville, TN 37232, USA
- Vanderbilt Institute of Chemical Biology, Vanderbilt University, Nashville, TN 37232, USA
- Vanderbilt Institute of Nanoscale Science and Engineering, Vanderbilt University, Nashville, TN 37235, USA
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20
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Cerebral Iron Deposition in Neurodegeneration. Biomolecules 2022; 12:biom12050714. [PMID: 35625641 PMCID: PMC9138489 DOI: 10.3390/biom12050714] [Citation(s) in RCA: 77] [Impact Index Per Article: 25.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Revised: 05/12/2022] [Accepted: 05/13/2022] [Indexed: 02/04/2023] Open
Abstract
Disruption of cerebral iron regulation appears to have a role in aging and in the pathogenesis of various neurodegenerative disorders. Possible unfavorable impacts of iron accumulation include reactive oxygen species generation, induction of ferroptosis, and acceleration of inflammatory changes. Whole-brain iron-sensitive magnetic resonance imaging (MRI) techniques allow the examination of macroscopic patterns of brain iron deposits in vivo, while modern analytical methods ex vivo enable the determination of metal-specific content inside individual cell-types, sometimes also within specific cellular compartments. The present review summarizes the whole brain, cellular, and subcellular patterns of iron accumulation in neurodegenerative diseases of genetic and sporadic origin. We also provide an update on mechanisms, biomarkers, and effects of brain iron accumulation in these disorders, focusing on recent publications. In Parkinson’s disease, Friedreich’s disease, and several disorders within the neurodegeneration with brain iron accumulation group, there is a focal siderosis, typically in regions with the most pronounced neuropathological changes. The second group of disorders including multiple sclerosis, Alzheimer’s disease, and amyotrophic lateral sclerosis shows iron accumulation in the globus pallidus, caudate, and putamen, and in specific cortical regions. Yet, other disorders such as aceruloplasminemia, neuroferritinopathy, or Wilson disease manifest with diffuse iron accumulation in the deep gray matter in a pattern comparable to or even more extensive than that observed during normal aging. On the microscopic level, brain iron deposits are present mostly in dystrophic microglia variably accompanied by iron-laden macrophages and in astrocytes, implicating a role of inflammatory changes and blood–brain barrier disturbance in iron accumulation. Options and potential benefits of iron reducing strategies in neurodegeneration are discussed. Future research investigating whether genetic predispositions play a role in brain Fe accumulation is necessary. If confirmed, the prevention of further brain Fe uptake in individuals at risk may be key for preventing neurodegenerative disorders.
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21
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Salvalaggio A, Silvestri E, Sansone G, Pinton L, Magri S, Briani C, Anglani M, Lombardi G, Zagonel V, Della Puppa A, Mandruzzato S, Corbetta M, Bertoldo A. Magnetic Resonance Imaging Correlates of Immune Microenvironment in Glioblastoma. Front Oncol 2022; 12:823812. [PMID: 35392230 PMCID: PMC8980808 DOI: 10.3389/fonc.2022.823812] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2021] [Accepted: 02/18/2022] [Indexed: 11/13/2022] Open
Abstract
Background Glioblastoma (GBM) is the most commonly occurring primary malignant brain tumor, and it carries a dismal prognosis. Focusing on the tumor microenvironment may provide new insights into pathogenesis, but no clinical tools are available to do this. We hypothesized that the infiltration of different leukocyte populations in the tumoral and peritumoral brain tissues may be measured by magnetic resonance imaging (MRI). Methods Pre-operative MRI was combined with immune phenotyping of intraoperative tumor tissue based on flow cytometry of myeloid cell populations that are associated with immune suppression, namely, microglia and bone marrow-derived macrophages (BMDM). These cell populations were measured from the central and marginal areas of the lesion identified intraoperatively with 5-aminolevulinic acid-guided surgery. MRI features (volume, mean and standard deviation of signal intensity, and fractality) were derived from all MR sequences (T1w, Gd+ T1w, T2w, FLAIR) and ADC MR maps and from different tumor areas (contrast- and non-contrast-enhancing tumor, necrosis, and edema). The principal components of MRI features were correlated with different myeloid cell populations by Pearson's correlation. Results We analyzed 126 samples from 62 GBM patients. The ratio between BMDM and microglia decreases significantly from the central core to the periphery. Several MRI-derived principal components were significantly correlated (p <0.05, r range: [-0.29, -0.41]) with the BMDM/microglia ratio collected in the central part of the tumor. Conclusions We report a significant correlation between structural MRI clinical imaging and the ratio of recruited vs. resident macrophages with different immunomodulatory activities. MRI features may represent a novel tool for investigating the microenvironment of GBM.
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Affiliation(s)
- Alessandro Salvalaggio
- Department of Neuroscience, University of Padova, Padova, Italy.,Padova Neuroscience Center, University of Padova, Padova, Italy
| | - Erica Silvestri
- Padova Neuroscience Center, University of Padova, Padova, Italy.,Department of Information Engineering, University of Padova, Padova, Italy
| | - Giulio Sansone
- Department of Neuroscience, University of Padova, Padova, Italy
| | - Laura Pinton
- Veneto Institute of Oncology - Istituto di Ricovero e Cura a Carattere Scientifico (IOV-IRCCS), Padova, Italy
| | - Sara Magri
- Department of Surgery, Oncology and Gastroenterology, University of Padova, Padova, Italy
| | - Chiara Briani
- Department of Neuroscience, University of Padova, Padova, Italy
| | | | - Giuseppe Lombardi
- Department of Oncology, Oncology 1, Veneto Institute of Oncology IOV-IRCCS, Padova, Italy
| | - Vittorina Zagonel
- Department of Oncology, Oncology 1, Veneto Institute of Oncology IOV-IRCCS, Padova, Italy
| | - Alessandro Della Puppa
- Neurosurgery, Department of NEUROFARBA, University Hospital of Careggi, University of Florence, Florence, Italy
| | - Susanna Mandruzzato
- Veneto Institute of Oncology - Istituto di Ricovero e Cura a Carattere Scientifico (IOV-IRCCS), Padova, Italy.,Department of Surgery, Oncology and Gastroenterology, University of Padova, Padova, Italy
| | - Maurizio Corbetta
- Department of Neuroscience, University of Padova, Padova, Italy.,Padova Neuroscience Center, University of Padova, Padova, Italy.,Venetian Institute of Molecular Medicine, Fondazione Biomedica, Padova, Italy
| | - Alessandra Bertoldo
- Padova Neuroscience Center, University of Padova, Padova, Italy.,Department of Information Engineering, University of Padova, Padova, Italy
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22
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Donatelli G, Costagli M, Cecchi P, Migaleddu G, Bianchi F, Frumento P, Siciliano G, Cosottini M. Motor cortical patterns of upper motor neuron pathology in amyotrophic lateral sclerosis: A 3 T MRI study with iron-sensitive sequences. NEUROIMAGE: CLINICAL 2022; 35:103138. [PMID: 36002961 PMCID: PMC9421531 DOI: 10.1016/j.nicl.2022.103138] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 07/05/2022] [Accepted: 07/27/2022] [Indexed: 11/11/2022] Open
Abstract
M1 regions associated with the body site of onset are frequently affected at MRI. The simultaneous involvement of both homologous M1 regions is frequent. The T2* hypointensity in non-contiguous M1 regions seems rare.
Background Patterns of initiation and propagation of disease in Amyotrophic Lateral Sclerosis (ALS) are still partly unknown. Single or multiple foci of neurodegeneration followed by disease diffusion to contiguous or connected regions have been proposed as mechanisms underlying symptom occurrence. Here, we investigated cortical patterns of upper motor neuron (UMN) pathology in ALS using iron-sensitive MR imaging. Methods Signal intensity and magnetic susceptibility of the primary motor cortex (M1), which are associated with clinical UMN burden and neuroinflammation, were assessed in 78 ALS patients using respectively T2*-weighted images and Quantitative Susceptibility Maps. The signal intensity of the whole M1 and each of its functional regions was rated as normal or reduced, and the magnetic susceptibility of each M1 region was measured. Results The highest frequencies of T2* hypointensity were found in M1 regions associated with the body sites of symptom onset. Homologous M1 regions were both hypointense in 80–93 % of patients with cortical abnormalities, and magnetic susceptibility values measured in homologous M1 regions were strongly correlated with each other (ρ = 0.88; p < 0.0001). In some cases, the T2* hypointensity was detectable in two non-contiguous M1 regions but spared the cortex in between. Conclusions M1 regions associated with the body site of onset are frequently affected at imaging. The simultaneous involvement of both homologous M1 regions is frequent, followed by that of adjacent regions; the affection of non-contiguous regions, instead, seems rare. This type of cortical involvement suggests the interhemispheric connections as one of the preferential paths for the UMN pathology diffusion in ALS.
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